
#Set up

#s1
rm(list=ls())

setwd ("C:/Users/cecidy/Google Drive/ZH_analysis/")

source("ZHfunctions.R")
options(scipen=9999)

ZeroHunger <- read.csv("Data/ZHdf/ZeroHungerDF_2020.csv",header=TRUE, sep=",")

#s2
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#start1_

#BF and Infant mortality 04 to 13 robustness sample

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#Make a nonBF ZHvariable!

ZeroHunger$ZHPerCapTotal04to13for2004Analysis1000NOBF <- with(ZeroHunger,ZHPerCapTotal04to13for2004Analysis1000-BFPerCapTotal04to13for2004Analysis1000)

#######################################################################################################################

#1. Prepare each dataframe

#Get Rural municipalities, dont include those very large municipalities
Poverty04 <- subset(ZeroHunger, PopDensity20042004Analysis<=150 & SizeKm22004Analysis<=10000)

#Get list of vars
load(file = "Data/ForSamples/MPI04ListOnly04.rda")
load(file = "Data/ForSamples/Basic04.rda")

RuralPoverty04 <- subset(Poverty04, select=c("BFPerCapTotal04to13for2004Analysis1000","ZHPerCapTotal04to13for2004Analysis1000NOBF",Basic04,
                                             "SIABCriteriafor2004AnalysisMunNEW",MPI04ListOnly04,
                                             "InfantMortality2004for2004Analysis100",
                                             "InfantMortality2013for2004Analysis100"))
names(RuralPoverty04)

RuralPoverty04 <- na.omit(RuralPoverty04)

#Then take away the ones affected by rural credit
load(file = "Data/ForSamples/EcludeForRCProb04.rda")

RuralPoverty04 <- subset(RuralPoverty04,(!(IBGECode7digit %in% EcludeForRCProb04)))

#Then do the quality
RuralPoverty04quality <- subset(RuralPoverty04, SIABCriteriafor2004AnalysisMunNEW==1)

#############################################################################################

#get in MPI13, do the MPI function. 
load(file = "Data/ForSamples/MPI04ListOnly13.rda")

TempVars <- subset(Poverty04,select=c("IBGECode7digit",MPI04ListOnly13))
RuralPoverty04quality <- merge(x=RuralPoverty04quality,y=TempVars, by="IBGECode7digit",all.x=T)

#############################################################################################

#Make logged variables

#First make MPI with the correct 0s
test <- NrOfZeroALL(RuralPoverty04quality)#This gives me all the vars with 0s
MPIadd <- test[[1]]#looks like all need 0, can this be?
Otheradd <- test[[2]]

#For the MPI because Ive not previously renamed when adding I just override the vars with 0 with the minimum/2
WithAdded <- lapply(MPIadd,function(y) AddConstant(RuralPoverty04quality,y,addName=""))
WithAdded <- data.frame(do.call("cbind",WithAdded))

RuralPoverty04quality[,MPIadd] <- WithAdded[,MPIadd]#just override

#Then I need to make the MPI - OBS 04 FUNCTION
RuralPoverty04quality <- MakeMPI04(RuralPoverty04quality)

#######################################################################################################################

#Then for the others I cbind them on, and because Ive not included any of previous noCero there no duplicates
RuralPoverty04quality <- cbind(RuralPoverty04quality,lapply(Otheradd,function(y) AddConstant(RuralPoverty04quality,y,addName="nocero")))#

###########################################################################################################################

#And finally I need to log variables - get a list of all names

OtheraddCero <- paste(Otheradd,"nocero",sep="")#here which are names no cero

#All these need log
cols.log <- c("BFPerCapTotal04to13for2004Analysis1000","ZHPerCapTotal04to13for2004Analysis1000NOBF","MPI2004for2004Analysis",
              "GDPPerCapPublicrealN2004for2004Analysis1000",OtheraddCero,"RemoteMinPopFifty2004Analysis",
              "PopDensity20042004Analysis","SizeKm22004Analysis","MeanSlopefor2004analysis","MeanElevationfor2004analysis")
addlog <- function(x) log10(x)
Log10variables <- data.frame(sapply(RuralPoverty04quality[cols.log],addlog))
colnames(Log10variables) <- paste(colnames(Log10variables), "log10",sep="")

#Get all onto ZH
RuralPoverty04quality <- cbind(RuralPoverty04quality,Log10variables)
head(RuralPoverty04quality)

###########################################################################################################################

#Need to take away all the MPI13 again, if not then later it messes up the size
AllMPI13 <- c(MPI04ListOnly13,"MPIHealthChildMalnutrition2013for2004Analysis","MPIHealth2013for2004Analysis","MPI2013for2004Analysis",
              "MPI2013for2004Analysis","MPILivingStandard2013for2004Analysis")
RuralPoverty04quality <- RuralPoverty04quality[, !names(RuralPoverty04quality) %in% AllMPI13]

#####################################################################################################

#Remove those I dont need
Log10variables <- NULL
Poverty04 <- NULL
TempVars <- NULL
WithAdded <- NULL

#####################################################################

#I make it on log here but that I might have to go back and change after checking for log or not!
RuralPoverty04quality<- getDataFrame(RuralPoverty04quality,"contr.Sum",
                                     contrastVar1="stateName",contrastVar2="Biomefor2004Analysis",setRefToMostCommonLevel,
                                     BaseVar="BFPerCapTotal04to13for2004Analysis1000log10",
                                     MainName="BFPerCapTotal04to13for2004Analysis1000log10scaledMain")

#fin1_
###############################################################################################################

#start2_

#Prepare vectors of variables for regressions

###############################################################################################################

Depvar <- "InfantMortality2013for2004Analysis100"
DepVarLog <- "InfantMortality2013for2004Analysis100"

IndepVarLin <- "BFPerCapTotal04to13for2004Analysis1000log10scaledMain"#already scaled so dont need to do it again

baselineDepVar <- "scale(InfantMortality2004for2004Analysis100)"

stateName <- "stateName"

intLin <- c("BFPerCapTotal04to13for2004Analysis1000log10scaledMain","stateName")
intLin <- paste(intLin,collapse="*")

xvars <- c("scale(MPI2004for2004Analysislog10)",
           "scale(GDPPerCapPublicrealN2004for2004Analysis1000log10)",
           "scale(TotalKcal2004percapperdaynocerolog10)",
           "scale(TotalHectareCrops2004for2004Analysisnocerolog10)","scale(TotalHectarePasture2006for2004Analysisnocerolog10)",
           "scale(TotalHectareFarmsLess502006for2004Analysisnocerolog10)","scale(RemoteMinPopFifty2004Analysislog10)",
           "scale(ChangeCummulativeSPEI02and04to11and13for2004Analysis)","scale(TotRCpcNOPRONAF04to13for2004Analysis1000nocerolog10)",
           "scale(MeanSlopefor2004analysislog10)","scale(MeanElevationfor2004analysislog10)",
           "scale(PopDensity20042004Analysislog10)","scale(SizeKm22004Analysislog10)","scale(ZHPerCapTotal04to13for2004Analysis1000NOBFlog10)",
           "Biomefor2004Analysis")

#For CBPS models
cbpsDepVar <- "BFPerCapTotal04to13for2004Analysis1000log10"
cbpsbaselineDepVar <- "InfantMortality2004for2004Analysis100"

cbpsVars <- c("MPI2004for2004Analysislog10",
              "GDPPerCapPublicrealN2004for2004Analysis1000log10","TotalKcal2004percapperdaynocerolog10",
              "TotalHectareCrops2004for2004Analysisnocerolog10","TotalHectarePasture2006for2004Analysisnocerolog10",
              "TotalHectareFarmsLess502006for2004Analysisnocerolog10","RemoteMinPopFifty2004Analysislog10",
              "ChangeCummulativeSPEI02and04to11and13for2004Analysis","TotRCpcNOPRONAF04to13for2004Analysis1000nocerolog10",
              "MeanSlopefor2004analysislog10","MeanElevationfor2004analysislog10",
              "PopDensity20042004Analysislog10","SizeKm22004Analysislog10","ZHPerCapTotal04to13for2004Analysis1000NOBFlog10",
              "stateName","Biomefor2004Analysis")

#########################################################################################################################

#fin2_

#temp1
#temp2

################################################################################################################################

